Hyperspectral remote sensing (with more than a dozen and up to hundreds of spectral bands) is important for land cover classification, target detection, and applications in natural resources, forestry, agriculture, geology, and military. However, (1) limited spatial resolution, and (2) enormous data volume are two of the major limitations of hyperspectral remote sensing, which significantly hinder the collection and application of hyperspectral images in a broad range of areas. For large ground coverage, the data volume will be even greater.
To increase the spatial resolution, a prior art solution for airborne hyperspectral sensors is to fly at a lower altitude but at the cost of reducing the ground coverage (i.e. the ground coverage is narrower for each flight as compared to images taken at a higher attitude). For satellite sensors, the solution is to increase the focal length of the lens, which also significantly reduces the ground coverage per orbit circle. The reduction of ground coverage means a reduction in the efficiency of remote sensing data collection. More flight time is needed to cover the same area.
Therefore, to overcome the limitation of spatial resolution without narrowing the ground coverage, research on pixel unmixing of hyperspectral images has been an active topic for decades, in order to interpret spectral information that is smaller than one ground pixel. To date, prior art pixel unmixing techniques still need to be supported by some known information from other sources. Nonetheless, only very limited success has been achieved [01] [02].
On the other hand, to increase the spatial resolution of hyperspectral (“HS”) images, some researchers have recently begun to explore the potential of using pan-sharpening techniques to fuse panchromatic (“Pan”) images with a few selected hyperspectral bands [03] [04] [05]. A few others have used selected multispectral (“MS”) bands to fuse with selected HS bands [06]. Some existing pan-sharpening techniques were used in the fusions. Although more detail can be seen in the fusion results, their quality is still poor with obvious noise, colour distortion, and/or unnatural integration between spectral and spatial information.
Hyperspectral imaging typically involves relatively large volumes of data. To reduce the data volume, a common solution is the use of data compression (such as JPEG format). But, if lossless compression is used, the compression rate will not be high. Not much data volume can be reduced. If lossy compression is used, some image information will be lost, which is not acceptable for most remote sensing applications.
In view of the foregoing, there is a need for an improved sensor system and method to enlarge ground coverage, but still keep the spatial resolution and maintain manageable data volume for hyperspectral images.